为什么Apache Spark为什么在嵌套结构中读取不必要的Parquet列? [英] Why does Apache Spark read unnecessary Parquet columns within nested structures?

问题描述

我的团队正在构建一个ETL流程，以使用Spark将原始的定界文本文件加载到基于Parquet的数据湖"中. Parquet列存储的承诺之一是查询将只读取必要的列条纹".

My team is building an ETL process to load raw delimited text files into a Parquet based "data lake" using Spark. One of the promises of the Parquet column store is that a query will only read the necessary "column stripes".

但是我们看到正在读取嵌套模式结构的意外列.

But we're seeing unexpected columns being read for nested schema structures.

为了演示，这是一个使用Scala和Spark 2.0.1 shell的POC:

To demonstrate, here is a POC using Scala and the Spark 2.0.1 shell:

// Preliminary setup
sc.setLogLevel("INFO")
import org.apache.spark.sql.types._
import org.apache.spark.sql._

// Create a schema with nested complex structures
val schema = StructType(Seq(
    StructField("F1", IntegerType),
    StructField("F2", IntegerType),
    StructField("Orig", StructType(Seq(
        StructField("F1", StringType),
        StructField("F2", StringType))))))

// Create some sample data
val data = spark.createDataFrame(
    sc.parallelize(Seq(
        Row(1, 2, Row("1", "2")),
        Row(3, null, Row("3", "ABC")))),
    schema)

// Save it
data.write.mode(SaveMode.Overwrite).parquet("data.parquet")

然后，我们将文件读回到DataFrame并投影到列的子集:

Then we read the file back into a DataFrame and project to a subset of columns:

// Read it back into another DataFrame
val df = spark.read.parquet("data.parquet")

// Select & show a subset of the columns
df.select($"F1", $"Orig.F1").show

运行此命令时，我们将看到预期的输出:

When this runs we see the expected output:

+---+-------+
| F1|Orig_F1|
+---+-------+
|  1|      1|
|  3|      3|
+---+-------+

但是...查询计划显示的故事略有不同:

But... the query plan shows a slightly different story:

优化计划"显示:

val projected = df.select($"F1", $"Orig.F1".as("Orig_F1"))
projected.queryExecution.optimizedPlan
// Project [F1#18, Orig#20.F1 AS Orig_F1#116]
// +- Relation[F1#18,F2#19,Orig#20] parquet

说明"显示:

projected.explain
// == Physical Plan ==
// *Project [F1#18, Orig#20.F1 AS Orig_F1#116]
// +- *Scan parquet [F1#18,Orig#20] Format: ParquetFormat, InputPaths: hdfs://sandbox.hortonworks.com:8020/user/stephenp/data.parquet, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<F1:int,Orig:struct<F1:string,F2:string>>

在执行过程中产生的INFO日志还确认Orig.F2列被意外读取:

And the INFO logs produced during execution also confirm that the Orig.F2 column is unexpectedly read:

16/10/21 15:13:15 INFO parquet.ParquetReadSupport: Going to read the following fields from the Parquet file:

Parquet form:
message spark_schema {
  optional int32 F1;
  optional group Orig {
    optional binary F1 (UTF8);
    optional binary F2 (UTF8);
  }
}

Catalyst form:
StructType(StructField(F1,IntegerType,true), StructField(Orig,StructType(StructField(F1,StringType,true), StructField(F2,StringType,true)),true))

根据 Dremel纸和镶木地板文档中，用于复杂嵌套结构的列应独立存储和独立检索.

According to the Dremel paper and the Parquet documentation, columns for complex nested structures should be independently stored and independently retrievable.

问题:

1. 此行为是否是当前Spark查询引擎的限制?换句话说，Parquet是否支持最佳执行此查询，但是Spark的查询计划程序是幼稚的?
2. 或者，这是否是当前Parquet实施的局限性?
3. 或者，我是否没有正确使用Spark API?
4. 或者，我是否误解了Dremel/Parquet列存储应该如何工作?

可能相关:为什么查询性能与Spark SQL中的嵌套列有所不同吗?

推荐答案

目前这是对Spark查询引擎的限制，相关的JIRA票据在下面，spark仅处理Parquet中简单类型的谓词下推，而不处理嵌套的StructTypes

It's a limitation on the Spark query engine at the moment, the relevant JIRA ticket is below, spark only handles predicate pushdown of simple types in Parquet, not nested StructTypes

https://issues.apache.org/jira/browse/SPARK-17636

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